Learning from the Limits of an Adjudicatory Strategy for Resolving United States?Canada Fisheries Conflicts: Lessons from the Gulf of Maine
Bibliographic record
Abstract
Growing concern about the world's fisheries and the capacity of international institutions to sustain them led us to reexamine the use of a World Court Chamber to resolve a boundary conflict between the United States and Canada in the Gulf of Maine.Among that case's uniquefeatures was an unprecedented argument, mounted by the United States, that turned fisheries conservation and management into a legal principle for boundary delimitation.We raise the question of whether the World Court is an appropriate institution for addressing problems of managing and conserving fisheries.To test its appropriateness, we examine the rhetorical practices associated with that institution as manifested in the Gulf of Maine case.Those practices include (1) framing the natural resource problem primarily as a political problem, (2) use of strategically designed, lineararguments on behalfofpre-established claims that conceal system icfeatures ofthefisheries problem, (3) use of evaluative standards based on politics and equity but without regard to ecological concerns, and (4) selective use of science to support predetermined claims rather than selecting claims based on an understanding of the problem's ecological dimensions.These practices indicate that the fisheries conflict did not belong in the adjudicatory process, especially since an important alternative was then and is still available.Weargue that institutional arrangements modeled after the Convention on Great Lakes Fisheries and the Great Lakes Fishery Commission have greater capacity to induce rhetorical practices more conducive to addressing the fisheries problems in the Gulf of Maine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".